AIMC Topic: Humans

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Deep Learning to Predict Mortality After Cardiothoracic Surgery Using Preoperative Chest Radiographs.

The Annals of thoracic surgery
BACKGROUND: The Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) estimates mortality risk only for certain common procedures (eg, coronary artery bypass or valve surgery) and is cumbersome, requiring greater thanĀ 60 inputs. We hypo...

A deep learning method to assist with chronic atrophic gastritis diagnosis using white light images.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Chronic atrophic gastritis is a common preneoplastic condition of the stomach with a low detection rate during endoscopy.

Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: To create a new artificial intelligence approach based on deep learning (DL) from multiparametric MRI in the differential diagnosis of common parotid tumors.

All-cause mortality prediction in T2D patients with iTirps.

Artificial intelligence in medicine
Mortality in the type II diabetic elderly population can sometimes be prevented through intervention, for which risk assessment through predictive modeling is required. Since Electronic Health Records data are typically heterogeneous and sparse, the ...

Social impact and governance of AI and neurotechnologies.

Neural networks : the official journal of the International Neural Network Society
Advances in artificial intelligence (AI) and brain science are going to have a huge impact on society. While technologies based on those advances can provide enormous social benefits, adoption of new technologies poses various risks. This article fir...

A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: To propose a fully automated deep learning (DL) framework for the vertebral morphometry and Cobb angle measurement from three-dimensional (3D) computed tomography (CT) images of the spine, and validate the proposed framework on an external d...

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

Computers in biology and medicine
BACKGROUND: COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wilie...

Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac DTI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac diffusion tensor imaging (cDTI) provides invaluable information about the state of myocardial microstructure. For further clinical dissemination, free-breathing acquisitions are desired, which however require image registration prior to tenso...

Treatment plan prediction for lung IMRT using deep learning based fluence map generation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Recently, it has been shown that automated treatment planning can be executed by direct fluence prediction from patient anatomy using convolutional neural networks. Proof of principle publications utilise a fixed dose prescription and fixed ...